Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

A multi-angle hierarchical differential evolution approach for multimodal optimization problems

Multimodal optimization problem (MMOP) is one of the most common problems in engineering practices that requires multiple optimal solutions to be located simultaneously. An efficient algorithm for solving MMOPs should balance the diversity and convergence of the population, so that the global optimal solutions can be located as many as possible. However, most of existing algorithms are easy to be trapped into local peaks and cannot provide high-quality solutions. To better deal with MMOPs, considerations on the solution quality angle and the evolution stage angle are both taken into account in this paper and a multi-angle hierarchical differential evolution (MaHDE) algorithm is proposed. Firstly, a fitness hierarchical mutation (FHM) strategy is designed to balance the exploration and exploitation ability of different individuals. In the FHM strategy, the individuals are divided into two levels (i.e., low/high-level) according to their solution quality in the current niche. Then, the low/high-level individuals are applied to different guiding strategies. Secondly, a directed global search (DGS) strategy is introduced for the low-level individuals in the late evolution stage, which can improve the population diversity and provide these low-level individuals with the opportunity to re-search the global peaks. Thirdly, an elite local search (ELS) strategy is designed for the high-level individuals in the late evolution stage to refine their solution accuracy. Extensive experiments are developed to verify the performance of MaHDE on the widely used MMOPs test functions i.e., CEC’2013. Experimental results show that MaHDE generally outperforms the compared state-of-the-art multimodal algorithms

Maximizing lifetime of range-adjustable wireless sensor networks: a neighborhood-based estimation of distribution algorithm

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS

Cardiovascular risk burden, dementia risk and brain structural imaging markers:a study from UK Biobank

Background:Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure, while the role of genetics and incident cardiovascular disease (CVD) remains unclear. Aims:To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample, and to explore the role of genetics and CVD onset. Methods:A prospective study among 354 654 participants free of CVD and dementia (2006–2010, mean age 56.4 years) was conducted within the UK Biobank, with brain magnetic resonance imaging (MRI) measurement available for 15 104 participants since 2014. CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score (FGCRS). Dementia diagnosis was ascertained from inpatient and death register data. Results:Over a median 12.0-year follow-up, 3998 all-cause dementia cases were identified. Higher FGCRS was associated with increased all-cause dementia risk after adjusting for demographic, major lifestyle, clinical factors and the polygenic risk score (PRS) of Alzheimer’s disease. Comparing the high versus low tertile of FGCRS, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.26 (1.12 to 1.41) for all-cause dementia, 1.67 (1.33 to 2.09) for Alzheimer’s disease and 1.53 (1.07 to 2.16) for vascular dementia (all ptrend&lt;0.05). Incident stroke and coronary heart disease accounted for 14% (95% CI: 9% to 21%) of the association between FGCRS and all-cause dementia. Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype. We observed an 83% (95% CI: 47% to 128%) higher all-cause dementia risk comparing the high–high versus low–low FGCRS–PRS category. For brain volumes, higher FGCRS was associated with greater log-transformed white matter hyperintensities, smaller cortical volume and smaller grey matter volume. Conclusions:Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes. The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia.</p

Cardiovascular risk burden, dementia risk and brain structural imaging markers:a study from UK Biobank

Background:Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure, while the role of genetics and incident cardiovascular disease (CVD) remains unclear. Aims:To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample, and to explore the role of genetics and CVD onset. Methods:A prospective study among 354 654 participants free of CVD and dementia (2006–2010, mean age 56.4 years) was conducted within the UK Biobank, with brain magnetic resonance imaging (MRI) measurement available for 15 104 participants since 2014. CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score (FGCRS). Dementia diagnosis was ascertained from inpatient and death register data. Results:Over a median 12.0-year follow-up, 3998 all-cause dementia cases were identified. Higher FGCRS was associated with increased all-cause dementia risk after adjusting for demographic, major lifestyle, clinical factors and the polygenic risk score (PRS) of Alzheimer’s disease. Comparing the high versus low tertile of FGCRS, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.26 (1.12 to 1.41) for all-cause dementia, 1.67 (1.33 to 2.09) for Alzheimer’s disease and 1.53 (1.07 to 2.16) for vascular dementia (all ptrend&lt;0.05). Incident stroke and coronary heart disease accounted for 14% (95% CI: 9% to 21%) of the association between FGCRS and all-cause dementia. Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype. We observed an 83% (95% CI: 47% to 128%) higher all-cause dementia risk comparing the high–high versus low–low FGCRS–PRS category. For brain volumes, higher FGCRS was associated with greater log-transformed white matter hyperintensities, smaller cortical volume and smaller grey matter volume. Conclusions:Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes. The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia.</p

Tunable multi-bands in twisted double bilayer graphene

The bandstructure of a material, playing an important role in its electron transport property, is usually governed by the lattice configuration. Materials with a field-effect tunable band, such as bilayer [1] and rhombohedral trilayer graphene [2, 3], are more flexible for electronic applications. Here, on dual-gated twisted double bilayer graphene (TDBG) samples with small twist angle around 1∘, we observe vertical electric-field-tunable bandstructures at multiple moiré fillings with bandgap values continuously varying from zero to tens of mili-electron volts. Moreover, within the first moiré filling on both electron and hole sides, the carrier transport deviates from Fermi liquid behavior, with measured resistivity exhibiting linear temperature dependence between 1.5 K and 50 K. Furthermore, under a vertical magnetic field, the coupling between the two bilayer graphene layers can also be turned on and off by a displacement field. Our results suggest TDBG with small twist angle is a platform for studying the evolution of multiple electric field tunable moiré bands and the resulting emergent correlated electronic phases

First Assessment of NOx Sources at a Regional Background Site in North China Using Isotopic Analysis Linked with Modeling

important role in the formation of atmospheric particles. Thus, NOx emission reduction is critical for improving air quality, especially in severely air-polluted regions (e.g., North China). In this study, the source of NOx was investigated by the isotopic composition (delta N-15) of particulate nitrate (p-NO3-) at Beihuangcheng Island (BH), a regional background site in North China. It was found that the delta N-15-NO3- (n = 120) values varied between -1.7 parts per thousand and +24.0 parts per thousand and the delta O-18-NO3- values ranged from 49.4 parts per thousand to 103.9 parts per thousand. On the basis of the Bayesian mixing model, 27.78 +/- 8.89%, 36.53 +/- 6.66%, 22.01 +/- 6.92%, and 13.68 +/- 3.16% of annual NOx could be attributed to biomass burning, coal combustion, mobile sources, and biogenic soil emissions, respectively. Seasonally, the four sources were similar in spring and fall. Biogenic soil emissions were augmented in summer in association with the hot and rainy weather. Coal combustion increased significantly in winter with other sources showing an obvious decline. This study confirmed that isotope-modeling by delta N-15-NO3- is a promising tool for partitioning NOx sources and provides guidance to policymakers with regard to options for NOx reduction in North China

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure